Autonomous vehicles use various computing systems to aid in the transport of passengers from one location to another. Some autonomous vehicles may require some initial input or continuous input from an operator, such as a pilot, driver, or passenger. Other systems, for example autopilot systems, may be used only when the system has been engaged, which permits the operator to switch from a manual driving mode (where the operator exercises a high degree of control over the movement of the vehicle) to an autonomous driving mode (where the vehicle essentially drives itself) to modes that lie somewhere in between.
When operating in an autonomous mode, these vehicles need to be able to accurately estimate their relative geographic location in the world. To do this, autonomous vehicles typically combine inertial pose estimation systems (e.g., using gyros, accelerometers, wheel encoders, GPS) and map-based localization approaches (e.g. generating a local map using onboard sensors and comparing these to a stored map to estimate where the vehicle is in the stored map). These approaches may result in noise or uncertainty in both the inertial pose estimation and localization stages that makes it difficult to achieve very high accuracy results. In addition, these approaches tend to focus on near range information so that a local map can be built up and compared against a stored map or because this is all that the sensor can detect with sufficient density of data points.